The method most often used in apparatuses which acquire spectral distribution data is to divide into bands a wavelength region (to be referred to as a “total wavelength region” hereinafter) to be measured such as a visible light region, acquire the spectral distribution information of each band, and acquire the spectral distribution information of the total wavelength region from the results.
FIG. 1 is a block diagram showing the arrangement of a multi-spectrum camera 207 for acquiring spectral distribution data.
A plurality of filters having spectral characteristics different in wavelength are arranged along the periphery of a disk-like rotary filter 202 in order of band wavelength. Reflected light from an object to be sensed passes through an optical system 201 and through one filter on the rotary filter 202, and is decomposed into light indicative of band information. By rotating the rotary filter 202 placed before a CCD sensor 204 by a driving motor 203, filters facing this CCD sensor 204 are switched, and input light is decomposed into light indicating each set of band information (that is, the information for each band).
The intensity of the light indicating band information is converted into a digital signal value by the CCD sensor 204 and an A/D converter 205, and stored as data in units of pixels into a data storage unit 206.
Generally, the smaller the number of filters of the rotary filter 202, the shorter the processing time required for sampling and the higher the speed of image sensing. Therefore, the number of filters is minimized.
As indicated by the solid lines in FIG. 2, the spectral distribution data acquired by the multi-spectrum camera 207 is the information of each band decomposed by the corresponding filter. Hence, this data cannot be directly used as the spectral distribution data of the total wavelength region. Therefore, it is necessary to acquire the spectral distribution data of the total wavelength region by interpolating the information of each band. Note that the spectral distribution data of the total wavelength region will be called “multi-spectral distribution data”, with respect to band information acquired by the multi-spectrum camera 207.
In this interpolation, as shown in FIG. 2, an intensity value corresponding to the intermediate wavelength of each band is defined as band information (indicated by ● in FIG. 2). These pieces of band information are connected by a curve (indicated by the broken line in FIG. 2) to obtain multi-spectral distribution data as the spectrum interpolation result. FIG. 2 shows the spectral characteristic acquired by the multi-spectrum camera 207 having six filters. In this case, multi-spectral distribution data must be estimated by using band information having six fixed filter characteristics.
Methods other than the above interpolation process are available by which the spectral distribution data of the total wavelength region is obtained by using band information. One method will be explained below.
A function for obtaining an output value at a wavelength λ of the spectral distribution data information of each decomposed band is defined as Ln(λ) where n=1, 2, . . . n corresponds to the information of each decomposed band. Spectral distribution data R(λ) of the total wavelength region is calculated as a linear sum at different wavelengths of Ln(λ) given by
                              R          ⁡                      (            λ            )                          =                              ∑            λ                    ⁢                      {                                          a1                ·                                  L1                  ⁡                                      (                    λ                    )                                                              +                              a2                ·                                  L2                  ⁡                                      (                    λ                    )                                                              +              …              +                              a                ⁢                                                                  ⁢                                  n                  ·                                      Ln                    ⁡                                          (                      λ                      )                                                                                            }                                              (        1        )            
where a1, a2, . . . , an are arbitrary coefficients which can be different from one wavelength λ to another
In addition to the above method, methods represented by the KL expanding method are known by which the spectral distribution data of the total wavelength region is obtained from band information. Accordingly, an optimum method is properly used in accordance with, e.g., the characteristics of band information.
A technique which realizes perfect color matching between environments differing in environmental illuminating light, i.e., environments having illuminating lights different in spectral distribution is disclosed in, e.g., Japanese Patent Laid-Open No. 9-172649. Since this technique uses spectral distribution data, it is necessary to acquire the multi-spectral distribution data of illuminating light by using the multi-spectrum camera 207 as shown in FIG. 1, i.e., to estimate the spectral distribution of illuminating light.
Estimating the spectral distribution requires not only the image sensing time of the multi-spectrum camera 207 but also, in order to process all band information acquired, large amounts of memories for storing all band information in units of pixels and working buffer memories. In addition, a processing time for processing all the band information is necessary.
Furthermore, to avoid errors produced by the interpolation process described above, it is necessary to use complicated interpolation and increase the number of band information. This increases the load (time) of image sensing and processing.